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loadFiles.py
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loadFiles.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Nov 10 17:32:04 2015
@author: AravindKumarReddy
"""
import os
import os.path as op
import pandas as pd
import json
import geocoder
import csv
import re
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import Normalizer
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.cross_validation import train_test_split
from sklearn.cluster import KMeans
def loadfiles(path):
directories = [path]
#files = []
tweetdata = []
while len(directories)!=0:
nextDir = directories.pop(0)
for i in os.listdir(nextDir):
current = op.join(nextDir,i)
if op.isfile(current):
ext = op.splitext(current)[-1].lower()
if ext == '.json':
#files.append(current)
with open(current,'r') as f:
for line in f:
try:
js = json.loads(line)
tweetdata.append([js['text'].encode('ascii','ignore'),js['location']['lat'],js['location']['lng']])
except:
print 'Corrupt json:', current
pass
else:
directories.append(current)
res = pd.DataFrame(tweetdata,columns=['text','lat','lng'])
res.to_csv('C:\Users\AravindKumarReddy\Downloads\SMMSample\\rawdata.csv',index=False,header=None)
#return tweetdata
def reverseGeocode(lat,lng):
#g = geocoder.w3w([lat,lng],method='reverse',key='EBFZ9ETX')
#return '-'.join(g.json.get('words',''))
g = geocoder.google([lat,lng],method='reverse')
return g.city if g.city else g.state
def mapLocation(lat,lng):
def convert(x):
x = int(x)
diff = abs(x%10)
# if diff > 5:
# x += 10 - diff
# else:
# x -= diff
return str(x-diff)
return ':'.join([convert(lng),convert(lat)])
def loadStopWords(path):
with open(path,'r') as file:
return {i.replace('\n','').strip():1 for i in list(file)}
wordLocDict = {}
stopWords = loadStopWords('stopwords.csv')
def wordsForChiFeatures(text,loc):
notAlphaNumeric = u'[^a-z0-9]'
for i in text.split(' '):
i= i.lower()
if len(i)>3 :
if not re.search(notAlphaNumeric,i) and i not in stopWords:
wordLocDict[(i,loc)] = wordLocDict.get((i,loc),0) + 1
def assignFeature(text, location,feature,N):
if feature in text.split(' '):
owc = wordLocDict.get((feature,location))
ownotc = sum( j for i,j in wordLocDict.items() if i[0]==feature and i[1]!=location)
onotwc = sum( j for i,j in wordLocDict.items() if i[0]!=feature and i[1]==location)
return (owc+ownotc)*(owc+onotwc)*(1/float(N))
else:
return 0
def main(path):
#tweetdata = loadfiles(path)
tweetdata = pd.read_csv(path,header=0,dtype=str, names = ['text','lat','lng','class'])[:50000]
#traindata, testdata = train_test_split(tweetdata,test_size=0.3, random_state=50)
size = len(tweetdata)
start = 7*size/10
trainclass = tweetdata['class'][:start]
testclass = tweetdata['class'][start:]
vectorizer = TfidfVectorizer(max_df=0.5,min_df=2,
stop_words='english',use_idf=True,encoding='utf-8',
decode_error='ignore',lowercase=True)
norm = Normalizer(copy=False)
tfids = vectorizer.fit_transform(tweetdata['text'])
normalized_tfids = norm.fit_transform(tfids)
ch2 = SelectKBest(chi2, k=1000)
#normalized_tfids = ch2.fit_transform(normalized_tfids,tweetdata['class'])
data = pd.DataFrame(normalized_tfids.toarray())
traindata = data[:start]
testdata = data[start:]
traindata = ch2.fit_transform(traindata,trainclass)
testdata = ch2.fit_transform(testdata,testclass)
#traindata= pd.DataFrame(traindata,columns=['text','lat','lng','class'])
#testdata = pd.DataFrame(testdata,columns=['text','lat','lng','class'])
#tweetdata['location'] = map(reverseGeocode, tweetdata['lat'],tweetdata['lng'])
# map(wordsForChiFeatures,tweetdata['text'], tweetdata['location'])
# totalCount = sum(j for j in wordLocDict.values() if j>1)
# for i,j in wordLocDict.items():
# # change 1 to any value as per requirement
# if j>5 :
# tweetdata[str(i)] = map(lambda x,y:assignFeature(x,y,i[0],totalCount),tweetdata['text'],tweetdata['location'])
# tweetdata.to_csv('liw.csv',header=True, index=False,encoding='utf-8')
#testdata= loadfiles('C:\Users\AravindKumarReddy\Downloads\SMMTest')
#traindata['location'] = map(mapLocation, traindata['lat'],traindata['lng'])
#testdata['location'] = map(mapLocation, testdata['lat'],testdata['lng'])
#train_tfids = vectorizer.fit_transform(traindata['text'])
#test_tfids = vectorizer.fit_transform(testdata['text'])
#train_tfids = norm.fit_transform(train_tfids)
#test_tfids = norm.fit_transform(test_tfids)
#km = KMeans(n_clusters=2000, init='k-means++', max_iter=100, n_init=1)
#km.fit(traindata[[1,2]])
#y = traindata['class']
nb = MultinomialNB(alpha=.1)
nb.fit(traindata,trainclass)
predictions = nb.predict(testdata)
print predictions
print '================================='
print testclass
print accuracy_score(testclass,predictions)
#test labels
#km.fit(testdata[[1,2]])
#print accuracy_score(testdata['class'],predictions)
#print accuracy_score(km.labels_,predictions)
#pd.DataFrame( confusion_matrix(testdata['location'],predictions)).to_csv('confusion_mat.csv')
#print predictions[1:200]
if __name__ == '__main__':
try:
if(len(sys.argv) != 2):
print 'Usage \'P1-a.py folderpath\''
sys.exit(-1)
else:
loadfiles(sys.argv[1])
except:
print ''